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Automation · ROIHow to calculate ROI on AI automation
Automation projects get approved or killed on the strength of an ROI estimate — but most estimates only count the obvious labor savings and miss half the real return. Here's a fuller framework.
The basic formula
annual savings = (hours saved per week × 52 × loaded hourly cost) − (implementation cost + ongoing running cost)
Loaded hourly cost = (monthly CTC × 1.3 overhead) ÷ 160 working hours — the same method we use in the ₹40-lakh spreadsheet.
What most estimates leave out
| Often missed | Why it matters |
|---|---|
| Error-cost reduction | Fewer mistakes means less rework, fewer disputes, less reputational cost |
| Speed-to-revenue impact | A faster quote or faster follow-up can directly lift conversion, not just save time |
| Opportunity cost of senior time | An hour of a manager's time freed is worth more than the hour itself if redirected to higher-value work |
| Scaling without headcount | Automation that avoids a future hire is a real, if less visible, return |
Common estimation mistakes
- Ignoring the ramp-up period. Full time savings rarely arrive in month one — model a realistic adoption curve.
- Underestimating maintenance. Automations need occasional updates as processes change; budget for it.
- Only counting the happy path. Exception handling takes real time too — don't assume 100% of cases automate cleanly.
A useful sanity check: if the payback period is under 6 months on labor savings alone, it's very likely worth doing. Between 6–18 months, the error-cost and revenue-speed factors above often tip the decision.
Where to start
Our free AI readiness assessment gives a directional score in 5 minutes; a full audit builds the real numbers for your specific processes.
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